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Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updates

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

Detecting orchestrated cyberattack campaigns that span multiple organizations traditionally requires sharing sensitive telemetry and threat intelligence across institutional boundaries and country borders, a barrier that Federated Learning removes by training shared threat detectors directly on local data. We propose FedIoC, a modular framework in which clients fold locally available structured threat indicators into their gradient updates; we instantiate the client-side encoder with a supervised contrastive loss over IoC-matched flows. Within each training batch, flows that match any known in

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